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Variations in land surface characteristics directly alter surface biophysical properties like albedo, roughness length, and canopy resistance, leading to changes in surface radiative and turbulent fluxes. These changes influence sensible and latent heat fluxes, which can further regulate surface temperature, evapotranspiration, and near-surface moisture transport. Thus, variations in surface fluxes associated with changes in land-surface properties can regulate convective instability, moisture convergence, and can modulate short-range rainfall characteristics and their predictability. Such land–atmosphere feedbacks are particularly important over the Indian region, where strong seasonal contrasts and heterogeneous land surfaces play a critical role in shaping rainfall variability. Hence, this study investigates the sensitivity of short-range precipitation forecasts over India to decadal changes in land use and vegetation during the pre-monsoon and monsoon periods. USGS 24-category land use data based on the 1994 landscape is used as the control run. Seven different simulation experiments are conducted using WRF model with various land use and vegetation data from MODIS and ISRO; ie: Experiment1 (MODIS 2001, USGS LAI and VF default), Experiment 2 (MODIS 2001, LAI and VF default), Experiment 3 (MODIS 2019, LAI and VF default), Experiment 4 (MODIS 2001, with Urban Class of 2019, LAI, and VF default), Experiment 5 (MODIS 2001, with water bodies of 2019, LAI, and VF default), Experiment 6 (MODIS 2019, LAI and VF of 2019), Experiment 7 (ISRO 2018-2019, VF and LAI default). A comprehensive assessment based on quantitative error metrics and categorical forecast skill scores demonstrates statistically significant improvements in rainfall forecast performance following the assimilation of updated land-use and vegetation datasets. These statistically robust improvements indicate that realistic representation of land-surface conditions contributes meaningfully to enhanced short-range precipitation predictability. The computed Extreme Dependency Index (EDI) values indicate an enhanced ability of the model to capture rare extreme rainfall events following the incorporation of updated land-use information. The incorporation of realistic land-use classifications derived from MODIS and ISRO datasets led to improved simulations of surface meteorological variables, including temperature, wind speed, relative humidity, surface pressure, and surface fluxes. Corresponding improvements were also observed in the vertical atmospheric profiles for wind, temperature, and specific humidity profiles. These enhancements indicate a more realistic depiction of land–atmosphere interactions and boundary-layer processes in the model.